The reliability of biomedical devices is closely linked to the quality and long-term stability of the electronic circuits that support their operation. Printed circuit boards (PCBs), in particular, can be affected by manufacturing imperfections, thermal stress and progressive ageing, which may lead to failures during the device life cycle. In this study, we present the design and simulation-based validation of an embedded acquisition circuit aimed at monitoring PCB electrical integrity in a non-invasive and remote manner. The presented solution is based on Hall-effect current sensing combined with a 16-bit analog-to-digital conversion stage and a digital communication interface managed by a Raspberry Pi. This configuration allows the system not only to acquire integrity-related electrical signals but also to process them locally and transmit them wirelessly for supervision purposes. A lightweight artificial intelligence model is implemented directly on the embedded platform to analyse the acquired signals and to classify different PCB operating conditions in real time. Simulation results show that the system is able to identify small current variations caused by micro-discontinuities and abnormal conductive paths. The classification accuracy exceeds 97% for PCB integrity states, confirming the suitability of the approach for remote monitoring, predictive maintenance and safety support in electromedical devices.
Filippo Laganà (Sun,) studied this question.